Identification of dysregulated gene clusters and pathways driving ocular surface squamous neoplasia progression
Bibliographic record
Abstract
Ocular Surface Squamous Neoplasia (OSSN) represents a spectrum of ocular malignancies that threaten vision and ocular integrity. To unravel the molecular mechanisms underlying OSSN progression, we conducted RNA-sequencing on conjunctival tissues from healthy individuals and patients with OSSN. Our analysis revealed marked alterations in the expression of genes implicated in inflammation, immune dysregulation, cell cycle regulation, and cellular stress responses. Notably, genes such as TP53, CXCL9, CXCL11, IL6, TNFα, MMP7, MMP9, GSTM1, IFNα, and IL1β showed significant dysregulation in OSSN samples compared to controls. Pathway enrichment analysis highlighted the activation of Interferon-α, Interferon-γ, and IL6/JAK-STAT3 signaling, alongside pathways regulating inflammatory response, p53 signaling, G2M checkpoint, and apical surface integrity. Together, these findings indicate that OSSN is characterized by a pro-inflammatory and proliferative transcriptomic profile driven by chronic immune signaling and disrupted cell cycle control. These findings provide novel insights into the transcriptional landscape of OSSN and identify key pathways that may be targeted for improved diagnosis and therapy. The molecular insights provided by this study can potentially inform stratified management approaches and aid in the development of novel treatments for this challenging ocular surface malignancy.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".